mlsanityData to 7 Oct 2026

On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems

arXiv 2107.08293 ↗First posted 17 Jul 2021cs.LG cs.IT math.ITcited by 2

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Abstract (arXiv)

We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Learning (DRL) and conventional optimization methods in finding optimal phase shifts of the IRS elements to maximize the user signal-to-noise (SNR) ratio. Furthermore, we evaluate the robustness of these methods to channel impairments and changes in the system. We demonstrate numerically that DRL solutions show more robustness to noisy channels and user mobility.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 17 Jul 2021.

Builds on

7 of 18 references.

References · 7

  1. 2016Layer Normalizationcited by 5,579
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2017Parameter Space Noise for Explorationcited by 158
  4. 2020Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learningcited by 38
  5. 2018Beamforming Optimization for Intelligent Reflecting Surface with Discrete Phase Shiftscited by 3
  6. 2020Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communicationscited by 1
  7. 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMAcited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issuescited by 12
  2. 2022Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environmentscited by 0